Abstract
In 2000, Chile introduced profound health reforms to achieve a more equitable and fairer system (GES plan). The reforms established a maximum waiting time between diagnosis and treatment for a set of diseases, described as an opportunity guarantee within the reform. If the maximum waiting time is exceeded, the patient is referred to another (private) facility and receives a voucher to cover the additional expenses. This voucher is paid by the health provider that had to do the procedure, which generally is a public hospital. In general, this reform has improved the service for patients with GES pathologies at the expense of patients with non-GES pathologies. These new conditions create a complicated planning scenario for hospitals, in which the hospital’s OR Manager must balance the fulfillment of these opportunity guarantees and the timely service of patients not covered by the guarantee. With the collaboration of the Instituto de Neurocirugía, in Santiago, Chile, we developed a mathematical model based on stochastic dynamic programming to schedule surgeries in order to minimize the cost of referrals to the private sector. Given the large size of the state space, we developed an heuristic to compute good solutions in reasonable time and analyzed its performance. Our experimental results, with both simulated and real data, show that our algorithm performs close to optimum and improves upon the current practice. When we compared the results of our heuristic against those obtained by the hospital’s OR manager in a simulation setting with real data, we reduced the overtime from occurring 21% of the time to zero, and the non-GES average waiting list’s length from 71 to 58 patients, without worsening the average throughput.
Similar content being viewed by others
References
Addis, B., Carello, G., Grosso, A., & Tànfani, E. (2016). Operating room scheduling and rescheduling: A rolling horizon approach. Flexible Services and Manufacturing Journal, 28(1–2), 206–232.
Aringhieri, R., Landa, P., Soriano, P., Tnfani, E., & Testi, A. (2015). A two level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research, 54, 21–34. https://doi.org/10.1016/j.cor.2014.08.014.
Astaraky, D., & Patrick, J. (2015). A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling. European Journal of Operational Research, 245(1), 309–319.
Azar, M., Barrera, J., Carrasco, R. A., & Mondschein, S. (2017). Operating room scheduling with variable procedure times. In Proceedings of the 13th workshop on models and algorithms for planning and scheduling problems. Seeon Abbey, Germany.
Beliën, J., & Demeulemeester, E. (2007). Building cyclic master surgery schedules with leveled resulting bed occupancy. European Journal of Operational Research, 176(2), 1185–1204.
Brandeau, M. L., Sainfort, F., & Pierskalla, W. P. (2004). Operations research and health care: A handbook of methods and applications (Vol. 70). Berlin: Springer.
Bruni, M., Beraldi, P., & Conforti, D. (2015). A stochastic programming approach for operating theatre scheduling under uncertainty. IMA Journal of Management Mathematics, 26(1), 99–119.
Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research, 201(3), 921–932.
Catalina De Améstica, R. (2014). Informe revela que un 56% de los pacientes “no Auge” lleva más de un año esperando una cirugía. Diario La Segunda. http://www.lasegunda.com/movil/detallenoticia.aspx?idnoticia=934670. Accessed 2 May 2018.
Denton, B., Viapiano, J., & Vogl, A. (2007). Optimization of surgery sequencing and scheduling decisions under uncertainty. Health Care Management Science, 10(1), 13–24.
Dexter, F., Dexter, E. U., Masursky, D., & Nussmeier, N. A. (2008). Systematic review of general thoracic surgery articles to identify predictors of operating room case durations. Anesthesia & Analgesia, 106(4), 1232–1241.
Duma, D., & Aringhieri, R. (2015). An online optimization approach for the real time management of operating rooms. Operations Research for Health Care, 7, 40–51.
Gomes, C., Almada-Lobo, B., Borges, J., & Soares, C. (2012). Integrating data mining and optimization techniques on surgery scheduling. In S. Zhou, S. Zhang, & G. Karypis (Eds.), ADMA (pp. 589–602). Berlin: Springer.
Guerriero, F., & Guido, R. (2011). Operational research in the management of the operating theatre: A survey. Health Care Management Science, 14(1), 89–114.
Hans, E., Wullink, G., Van Houdenhoven, M., & Kazemier, G. (2008). Robust surgery loading. European Journal of Operational Research, 185(3), 1038–1050.
Jebali, A., Alouane, H. A., & Ladet, P. (2006). Operating rooms scheduling. International Journal of Production Economics, 99(1–2), 52–62.
Landa, P., Aringhieri, R., Soriano, P., Tànfani, E., & Testi, A. (2016). A hybrid optimization algorithm for surgeries scheduling. Operations Research for Health Care, 8, 103–114.
Lenz, R. (2007). Proceso político de la reforma auge de salud en Chile: algunas lecciones para América Latina: una mirada desde la economía política. CiEPLAN Santiago de Chile.
Magerlein, J. M., & Martin, J. B. (1978). Surgical demand scheduling: A review. Health Services Research, 13(4), 418.
Min, D., & Yih, Y. (2010). An elective surgery scheduling problem considering patient priority. Computers & Operations Research, 37(6), 1091–1099.
Molina, J. M., & Framinan, J. M. (2009). Testing planning policies for solving the elective case scheduling phase: A real application. In Proceedings of the 35th international conference on operational research applied to health services (ORAHS). Leuven, Belgium
Patrick, J., & Puterman, M. L. (2008). Reducing wait times through operations research: Optimizing the use of surge capacity. Healthcare Policy, 3(3), 75.
Pinedo, M. L. (2012). Scheduling: Theory, algorithms, and systems (4th ed.). Berlin: Springer.
Samudra, M., Van Riet, C., Demeulemeester, E., Cardoen, B., Vansteenkiste, N., & Rademakers, F. E. (2016). Scheduling operating rooms: Achievements, challenges and pitfalls. Journal of Scheduling, 19(5), 493–525.
Santibáñez, P., Begen, M., & Atkins, D. (2007). Surgical block scheduling in a system of hospitals: An application to resource and wait list management in a British Columbia health authority. Health Care Management Science, 10(3), 269–282.
Stepaniak, P. S., Heij, C., & De Vries, G. (2010). Modeling and prediction of surgical procedure times. Statistica Neerlandica, 64(1), 1–18.
Tànfani, E., & Testi, A. (2010). A pre-assignment heuristic algorithm for the master surgical schedule problem (MSSP). Annals of Operations Research, 178, 105–119.
Testi, A., & Tanfani, E. (2009). Tactical and operational decisions for operating room planning: Efficiency and welfare implications. Health Care Management Science, 12, 363–373.
Vansteenkiste, N., Lamote, C., Vandersmissen, J., Luysmans, P., Monnens, P., De Voldere, G., et al. (2012). Reallocation of operating room capacity using the due-time model. Medical Care, 50(9), 779–784.
World Health Organization. (2017). Surgical care systems strengthening: Developing national surgical, obstetric and anaesthesia plans. Geneva: World Health Organization.
Zúñiga-Fajuri, A. (2007). Sistemas sanitarios y reforma AUGE en Chile. Acta Bioethica, 13(2), 237–245.
Acknowledgements
This work was partially funded by Anillo Project 1407 and Ingeniería 2030, Corfo.
Author information
Authors and Affiliations
Corresponding author
Appendices
Data analysis 2015–2016
1.1 Pathologies characterization
In the following section, we report the analysis performed for the case study in Sect. 7. Tables 5, 6, and 7 show the arrival rates per pathology, surgery times, and GES parameters, respectively.
1.2 Waiting list characterization
Figure 4a, b show the number of patients in each waiting time range at the beginning of the planning horizon for each GES diagnosis. Table 8 contains the number of non-GES patients on the waiting list at the beginning of the planning horizon.
Experimental results details
1.1 Simulation based performance analysis
Tables 9 and 10 show the results of the simulations conducted to investigate the effect of due date changes. The simulations were performed using real values for \(C_A\), \(C_B\), \(g_A\), and \(g_B\) and assuming that all physicians can operate on all pathologies. For each case, we simulated 10 years of 30 weeks each and computed the average values for each important metric while scaling \(g_A\) in Table 9 and scaling \(g_B\) in Table 10.
1.2 Performance validation
Tables 11 and 12 show additional details of the case study conducted at Instituto de Neurocirugía to validate the simulation results.
Rights and permissions
About this article
Cite this article
Barrera, J., Carrasco, R.A., Mondschein, S. et al. Operating room scheduling under waiting time constraints: the Chilean GES plan. Ann Oper Res 286, 501–527 (2020). https://doi.org/10.1007/s10479-018-3008-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-018-3008-7